Guo Qingbao, Xie Manli, Han Cong, Wang Qian-Nan, Bao Xiangyang, Duan Lian
Department of Neurosurgery, XI'AN NO.9 HOSPITAL, Xi'an, Shaanxi, China.
Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine Northwest University, Xi'an, China.
Int J Surg. 2025 Sep 1;111(9):5821-5833. doi: 10.1097/JS9.0000000000002677. Epub 2025 Jun 12.
Pediatric hemorrhagic moyamoya disease (MMD) is rare, and currently, no risk model exists for predicting preoperative bleeding. We aimed to develop a nomogram to predict the preoperative bleeding risk in children with MMD.
We retrospectively analyzed data from 1350 children diagnosed with MMD from January 2004 to December 2022 at our institution. After applying propensity score matching (PSM), 392 patients were selected for analysis, comprising 98 with hemorrhagic MMD and 294 with non-hemorrhagic MMD. The cohort was divided into training and internal validation cohorts. To construct the nomogram, variable selection was performed using the least absolute shrinkage and selection operator (LASSO), and the model was externally validated with an independent cohort of 70 children. We utilized multivariate logistic regression to determine odds ratios and 95% confidence intervals for preoperative bleeding risk. A predictive nomogram was then developed from the logistic model, with polynomial equations to quantify risk. The model's effectiveness was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analyses (DCAs). Inflection points for continuous variables were identified using restricted cubic spline (RCS) analysis.
The LASSO model demonstrated superior discriminative performance compared to six alternative models, achieving area under the curve values of 91.5% in the training cohort, 78.4% in the internal validation cohort, and 91.2% in the external validation cohort. Based on variables selected through the LASSO model, we developed a nomogram incorporating three critical factors: age at onset ( P = 0.001), anterior choroidal artery grades 1 ( P = 0.047) and 2 ( P < 0.001), and posterior communicating artery grades 1 ( P = 0.002) and 2 ( P = 0.032). Calibration plots indicated strong concordance between predicted and observed outcomes across both training and validation cohorts (Hosmer-Lemeshow P = 0.503), affirming the model's accuracy. Additionally, DCA highlighted the nomogram's clinical utility by effectively identifying patients at high risk. RCS analysis revealed age 8 as a pivotal inflection point ( P < 0.05), marking a significant increase in the risk of preoperative bleeding beyond this age.
The nomogram demonstrated high accuracy in predicting preoperative bleeding risk in pediatric patients with MMD. This predictive accuracy may enhance preoperative evaluation by surgeons, allowing for more proactive intervention and intensified monitoring of children at elevated risk of bleeding, thereby improving patient outcomes.
儿童出血性烟雾病（MMD）较为罕见，目前尚无预测术前出血的风险模型。我们旨在开发一种列线图，以预测MMD患儿的术前出血风险。
我们回顾性分析了2004年1月至2022年12月在我院确诊为MMD的1350例儿童的数据。应用倾向评分匹配（PSM）后，选择392例患者进行分析，其中98例为出血性MMD，294例为非出血性MMD。该队列分为训练队列和内部验证队列。为构建列线图，使用最小绝对收缩和选择算子（LASSO）进行变量选择，并在一个由70名儿童组成的独立队列中进行外部验证。我们采用多因素逻辑回归来确定术前出血风险的比值比和95%置信区间。然后根据逻辑模型开发了一个预测列线图，用多项式方程来量化风险。使用受试者工作特征曲线、校准图和决策曲线分析（DCA）评估模型的有效性。使用受限立方样条（RCS）分析确定连续变量的拐点。
与六个替代模型相比，LASSO模型显示出更好的判别性能，在训练队列中的曲线下面积值为91.5%，在内部验证队列中为78.4%，在外部验证队列中为91.2%。基于通过LASSO模型选择的变量，我们开发了一个包含三个关键因素的列线图：发病年龄（P = 0.001）、脉络膜前动脉1级（P = 0.047）和2级（P < 0.001），以及后交通动脉1级（P = 0.002）和2级（P = 0.032）。校准图表明，在训练队列和验证队列中，预测结果与观察结果之间具有很强的一致性（Hosmer-Lemeshow P = 0.503），证实了模型的准确性。此外，DCA突出了列线图通过有效识别高危患者的临床实用性。RCS分析显示8岁是一个关键拐点（P < 0.05），表明超过这个年龄术前出血风险显著增加。
该列线图在预测MMD患儿术前出血风险方面具有较高的准确性。这种预测准确性可能会提高外科医生的术前评估，从而对出血风险较高的儿童进行更积极的干预和加强监测，进而改善患者预后。
